Investors often mistake a large industry for a single, winner-take-all market. A vertical like legal tech isn't one market to be won; it's a $500 billion industry. Just as the legal profession has many specializations, the tech serving it will produce dozens of successful, specialized companies.

Valuations of AI companies may be artificially low because they're based on the token demand for simple chatbots. The real, explosive growth comes from reasoning models, agents, and multimodal generation, creating a near-infinite demand for tokens that is not yet priced in.

Unlike traditional B2B markets where only ~5% of customers are buying at any time, the AI boom has pushed nearly 100% of companies to seek solutions at once. This temporary gold rush warps perception of market size, creating a risk of over-investment similar to the COVID-era software bubble.

While sectors like legal AI receive intense media and investor attention, the global manufacturing market represents a vastly larger, greenfield opportunity at $20 trillion versus legal's $1 trillion. This makes industrial AI one of the most attractive yet underserved problem spaces for founders.

During a fundamental technology shift like the current AI wave, traditional market size analysis is pointless because new markets and behaviors are being created. Investors should de-emphasize TAM and instead bet on founders who have a clear, convicted vision for how the world will change.

The true market opportunity for AI is not merely replacing existing software but automating human labor. This reframes the total addressable market (TAM) from the ~$400 billion global software industry to the $13 trillion US-only labor market, representing a thirty-fold increase in potential value.

For venture capitalists investing in AI, the primary success indicator is massive Total Addressable Market (TAM) expansion. Traditional concerns like entry price become secondary when a company is fundamentally redefining its market size. Without this expansion, the investment is not worthwhile in the current AI landscape.

This provides a simple but powerful framework for venture investing. For companies in markets with demonstrably huge TAMs (e.g., AI coding), valuation is secondary to backing the winner. For markets with a more uncertain or constrained TAM (e.g., vertical SaaS), traditional valuation discipline and entry price matter significantly.

Jeffrey argues that while crypto is powerful for finance, its applications are limited. AI, or 'intelligence,' touches every aspect of human activity, from writing poetry to cooking. This gives it a vastly larger Total Addressable Market (TAM) and greater investment potential.